slides in pdf - Università degli Studi di Milano
... model parameters, store only the parameters, and discard the data (except possible outliers) Ex.: Log-linear models Non-parametric methods Do not assume models Major families: histograms, clustering, sampling, … ...
... model parameters, store only the parameters, and discard the data (except possible outliers) Ex.: Log-linear models Non-parametric methods Do not assume models Major families: histograms, clustering, sampling, … ...
SEQUENTIAL PATTERN ANALYSIS IN DYNAMIC BUSINESS
... Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining with diversified applications in many science and business fields, such as multimedia analysis (motion gesture/video seq ...
... Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining with diversified applications in many science and business fields, such as multimedia analysis (motion gesture/video seq ...
A Survey of Spatial Data Mining Methods Databases and
... The growing production of maps is generating huge volumes of data that exceed people's capacity to analyze them. It thus seems appropriate to apply knowledge discovery methods like data mining to spatial data. This recent technology is an extension of the data mining applied to alphanumerical data o ...
... The growing production of maps is generating huge volumes of data that exceed people's capacity to analyze them. It thus seems appropriate to apply knowledge discovery methods like data mining to spatial data. This recent technology is an extension of the data mining applied to alphanumerical data o ...
Data Mining Techniques for Medical Applications: A Survey
... different clusters, thus a global decorrelation cannot reduce this to traditional (uncorrelated) clustering. Correlations among subsets of attributes result in different spatial shapes of clusters. Hence, the similarity between cluster objects is defined by taking into account the local correlation ...
... different clusters, thus a global decorrelation cannot reduce this to traditional (uncorrelated) clustering. Correlations among subsets of attributes result in different spatial shapes of clusters. Hence, the similarity between cluster objects is defined by taking into account the local correlation ...
Spatial data mining as a tool for improving geographical models
... in this area is very restricted, this thesis should contribute as a survey on research, which has been recently done in this field. The biggest challenges of SDM are the spatial attributes of geographic data. Every object, situated in a geographical space is always related to another. This fact shou ...
... in this area is very restricted, this thesis should contribute as a survey on research, which has been recently done in this field. The biggest challenges of SDM are the spatial attributes of geographic data. Every object, situated in a geographical space is always related to another. This fact shou ...
Time Series and Seuence in detail
... Stream data mining tasks Multi-dimensional on-line analysis of streams Mining outliers and unusual patterns in stream data Clustering data streams Classification of stream data ...
... Stream data mining tasks Multi-dimensional on-line analysis of streams Mining outliers and unusual patterns in stream data Clustering data streams Classification of stream data ...
Distributed Higher Order Association Rule Mining Using
... The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed data ...
... The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed data ...
Analysis of Recommendation Algorithms for E
... use businesses achieve these goals. Schafer et al., [27] present a detailed taxonomy and examples of recommender systems used in E-commerce and how they can provide one-to-one personalization and at the same can capture customer loyalty. Knowledge Discovery in Databases (KDD). KDD techniques [10], a ...
... use businesses achieve these goals. Schafer et al., [27] present a detailed taxonomy and examples of recommender systems used in E-commerce and how they can provide one-to-one personalization and at the same can capture customer loyalty. Knowledge Discovery in Databases (KDD). KDD techniques [10], a ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... accuracy and detection rates when it‟s unable to detect all types of attacks correctly. To overcome this problem, authors have suggested a hybrid learning approach. In this approach they have combine two different technique one is K-Means clustering and second is Naïve Bayes classification. In this ...
... accuracy and detection rates when it‟s unable to detect all types of attacks correctly. To overcome this problem, authors have suggested a hybrid learning approach. In this approach they have combine two different technique one is K-Means clustering and second is Naïve Bayes classification. In this ...
The Use of Heuristics in Decision Tree Learning Optimization
... decision tree algorithm might fail. Interesting attempts have been done by R.C.Barros et al. [4]. They show an empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Hyperheuristics can automatically gene ...
... decision tree algorithm might fail. Interesting attempts have been done by R.C.Barros et al. [4]. They show an empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Hyperheuristics can automatically gene ...
DTW-D
... warped versions of some platonic ideal, possibly with other types of noise/distortions. ...
... warped versions of some platonic ideal, possibly with other types of noise/distortions. ...
APRIORI ALGORITHM AND FILTERED ASSOCIATOR IN
... itemsets before the beginning of a pass. The main difference from Apriori is that it does not use the database for counting support after the first pass. Rather, it uses an encoding of the candidate itemsets used in the previous pass denoted by Ck . In Apriori-TID, the candidate itemsets in Ck are s ...
... itemsets before the beginning of a pass. The main difference from Apriori is that it does not use the database for counting support after the first pass. Rather, it uses an encoding of the candidate itemsets used in the previous pass denoted by Ck . In Apriori-TID, the candidate itemsets in Ck are s ...
Practice of Data Mining
... their type of surgery performed 2. In each group, partition the continuous value attributes into discrete intervals or cells. Since the sample size is very small, we use a hybrid technique to determine the optimal number of cells and cell sizes. 3. Generate association rules for each patient group b ...
... their type of surgery performed 2. In each group, partition the continuous value attributes into discrete intervals or cells. Since the sample size is very small, we use a hybrid technique to determine the optimal number of cells and cell sizes. 3. Generate association rules for each patient group b ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.